Triangulation (social science)

About: Triangulation (social science) is a research topic. Over the lifetime, 4800 publications have been published within this topic receiving 103313 citations.

Mixing Qualitative and Quantitative Methods: Triangulation in Action @

Abstract: December 1979, volume 24 There is a distinct tradition in the literature on social science research methods that advocates the use of multiple methods. This form of research strategy is usually described as one of convergent methodology, multimethod/multitrait (Campbell and Fiske, 1959), convergent validation or, what has been called "triangulation" (Webb et al., 1 966). These various notions share the conception that qualitative and quantitative methods should be viewed as complementary rather than as rival camps. In fact, most textbooks underscore the desirability of mixing methods given the strengths and weaknesses found in single method designs.

The Use of Triangulation in Qualitative Research

Abstract: Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.

Two algorithms for constructing a Delaunay triangulation

Abstract: This paper provides a unified discussion of the Delaunay triangulation. Its geometric properties are reviewed and several applications are discussed. Two algorithms are presented for constructing the triangulation over a planar set ofN points. The first algorithm uses a divide-and-conquer approach. It runs inO(N logN) time, which is asymptotically optimal. The second algorithm is iterative and requiresO(N 2) time in the worst case. However, its average case performance is comparable to that of the first algorithm.

The point of triangulation.

Abstract: Purpose: To explore various types of triangulation strategies and to indicate when different types of triangulation should be used in research. Methods: Reviews included literature on triangulation and multimethod strategies published since 1960 and research books specifically focusing on triangulation. Findings: Triangulation is the combination of at least two or more theoretical perspectives, methodological approaches, data sources, investigators, or data analysis methods. The intent of using triangulation is to decrease, negate, or counterbalance the deficiency of a single strategy, thereby increasing the ability to interpret the findings. Conclusions: The use of triangulation strategies does not strengthen a flawed study. Researchers should use triangulation if it can contribute to understanding the phenomenon; however, they must be able to articulate why the strategy is being used and how it might enhance the study.

Three techniques for integrating data in mixed methods studies

Abstract: Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis